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What is Data Lake?

  • Amruta Bhaskar
  • Jun 1, 2021
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A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data, and run different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions.

Organizations that successfully generate business value from their data, will outperform their peers. An Aberdeen survey saw organizations who implemented a Data Lake outperforming similar companies by 9% in organic revenue growth. These leaders were able to do new types of analytics like machine learning over new sources like log files, data from click-streams, social media, and internet-connected devices stored in the data lake. This helped them to identify and act upon opportunities for business growth faster by attracting and retaining customers, boosting productivity, proactively maintaining devices, and making informed decisions.

 

First and foremost, data lakes are open format, so users avoid lock-in to a proprietary system like a data warehouse, which has become increasingly important in modern data architectures. Data lakes are also highly durable and low cost, because of their ability to scale and leverage object storage. Additionally, advanced analytics and machine learning on unstructured data are some of the most strategic priorities for enterprises today. The unique ability to ingest raw data in a variety of formats (structured, unstructured, semi-structured) along with the other benefits mentioned, make a data lake the clear choice for data storage.

When properly architected, data lakes enable the ability to:

Power data science and machine learning

Data lakes allow you to transform raw data into structured data that is ready for SQL analytics, data science and machine learning with low latency. Raw data can be retained indefinitely at a low cost for future use in machine learning and analytics.

Centralize, consolidate, and catalogue your data

A centralized data lake eliminates problems with data silos (like data duplication, multiple security policies and difficulty with collaboration), offering downstream users a single place to look for all sources of data.

Quickly and seamlessly integrate diverse data sources and formats

Any and all data types can be collected and retained indefinitely in a data lake, including batch and streaming data, video, image, binary files and more. And since the data lake provides a landing zone for new data, it is always up to date.

Democratize your data by offering users self-service tools

Data lakes are incredibly flexible, enabling users with completely different skills, tools and languages to perform different analytics tasks all at once.

Reasons for using Data Lake are:

  • With the onset of storage engines like Hadoop storing disparate information has become easy. There is no need to model data into an enterprise-wide schema with a Data Lake.
  • With the increase in data volume, data quality, and metadata, the quality of analyses also increases.
  • Data Lake offers business Agility
  • Machine Learning and Artificial Intelligence can be used to make profitable predictions.
  • It offers a competitive advantage to the implementing organization.
  • There is no data silo structure. Data Lake gives 360 degrees view of customers and makes analysis more robust.

Data lake challenges

Despite the pros of data lakes, a variety of challenges arise with data lakes that slow innovation and productivity. Data lakes lack the necessary features to ensure data quality and reliability. Seemingly simple tasks can drastically reduce a data lake’s performance and with poor security and governance features, data lakes fall short of business and regulatory needs.

  • Reliability issues

Without the proper tools in place, data lakes can suffer from data reliability issues that make it difficult for data scientists and analysts to reason about the data. These issues can stem from difficulty combining batch and streaming data, data corruption and other factors.

  • Slow performance

As the size of the data in a data lake increases, the performance of traditional query engines has traditionally gotten slower. Some of the bottlenecks include metadata management, improper data partitioning and others.

  • Lack of security features

Data lakes are hard to properly secure and govern due to the lack of visibility and ability to delete or update data. These limitations make it very difficult to meet the requirements of regulatory bodies.

The answer to the challenges of data lakes is the lakehouse, which solves the challenges of a data lake by adding a transactional storage layer on top. A lakehouse that uses similar data structures and data management features as those in a data warehouse but instead runs them directly on cloud data lakes. Ultimately, a lakehouse allows traditional analytics, data science and machine learning to coexist in the same system, all in an open format.

A lakehouse enables a wide range of new use cases for cross-functional enterprise-scale analytics, BI and machine learning projects that can unlock massive business value. Data analysts can harvest rich insights by querying the data lake using SQL, data scientists can join and enrich data sets to generate ML models with ever greater accuracy, data engineers can build automated ETL pipelines, and business intelligence analysts can create visual dashboards and reporting tools faster and easier than before. These use cases can all be performed on the data lake simultaneously, without lifting and shifting the data, even while new data is streaming in.

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